Sensor systems and methods for sewn product processing apparatus

ABSTRACT

Due to rapid advancement in computing technology of both hardware and software, the labor intensive sewing process has been transformed into a technology-intensive automated process. During an automated process, processing of a work piece can be visually monitored using a sensor system. Data gathered from the sensor system can be used to infer the condition and/or position of the work piece in the work area using, e.g., models of a sensor profile. Evaluation can be carried out before processing begins, during processing and/or after processing of the work piece. Examples of systems and methods are described that provide for initially and successively matching the model of the expected shape for a product work piece to a set of sensor readings of the work piece in order to determine the position of the work piece and support a variety of useful features.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to, and the benefit of, co-pending U.S.non-provisional application entitled “Sensor Systems and Methods forSewn Product Processing Apparatus” having Ser. No. 16/681,588, filedNov. 12, 2019, the entirety of which is hereby incorporated byreference.

FIELD OF THE DISCLOSURE

The present disclosure is generally related to systems, apparatuses, andmethods that cut and/or sew a product and, more particularly, is relatedto systems, apparatuses, and methods that determine the location of aworkpiece within a work area.

BACKGROUND

A common challenge in automating more complex processes in sewn productmanufacturing is that the apparatus, such as a sewing robot, mustdetermine the location of a sewn product work piece within the sewingrobot work area. There has been a limitation in the art of tracking thework piece, for example a sewn product such as material, fabric, ortextile, throughout the automated sewn product process. Also, it isdifficult to determine capabilities of the sewn product processapparatus and the steps that need to be taken in order to successfullycreate the desired product.

The value of this concept is that through captured sensor data thesystem can apply models of sensors as well as models of robot system toguide the robot system as well as determine if the process beingperformed by the sewn product making apparatus is acceptable.

The subject matter discussed in the background section should not beassumed to be prior art merely as a result of its mention in thebackground section. Similarly, a problem mentioned in the backgroundsection or associated with the subject matter of the background sectionshould not be assumed to have been previously recognized in the priorart. The subject matter in the background section merely representsdifferent approaches, which in and of themselves may also correspond toimplementations of the claimed technology.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate various examples of systems,methods, and embodiments of various other aspects of the disclosure. Anyperson with ordinary skills in the art will appreciate that theillustrated element boundaries (e.g., boxes, groups of boxes, or othershapes) in the figures represent one example of the boundaries. It maybe that in some examples one element may be designed as multipleelements or that multiple elements may be designed as one element. Insome examples, an element shown as an internal component of one elementmay be implemented as an external component in another, and vice versa.Furthermore, elements may not be drawn to scale. Non-limiting andnon-exhaustive descriptions are described with reference to thefollowing drawings. The components in the figures are not necessarily toscale, emphasis instead being placed upon illustrating principles.Moreover, in the drawings, like reference numerals designatecorresponding parts throughout the several views.

FIG. 1 illustrates an example of a vision system, according to variousembodiments of the present disclosure.

FIG. 2 illustrates an example of a vision base module, according tovarious embodiments of the present disclosure.

FIG. 3 illustrates an example of an incoming inspection module,according to various embodiments of the present disclosure.

FIG. 4 illustrates an example of an inline process module, according tovarious embodiments of the present disclosure.

FIG. 5 illustrates an example of a quality inspection module, accordingto various embodiments of the present disclosure.

DETAILED DESCRIPTION

Disclosed herein are various examples related to vision system which canbe used for automation of sewing. Some embodiments of this disclosure,illustrating all its features, will now be discussed in detail. Thewords “comprising,” “having,” “containing,” and “including,” and otherforms thereof, are intended to be equivalent in meaning and be openended in that an item or items following any one of these words is notmeant to be an exhaustive listing of such item or items, or meant to belimited to only the listed item or items.

It must also be noted that as used herein and in the appended claims,the singular forms “a,” “an,” and “the” include plural references unlessthe context clearly dictates otherwise. Although any systems and methodssimilar or equivalent to those described herein can be used in thepractice or testing of embodiments of the present disclosure, thepreferred, systems, and methods are now described.

Embodiments of the present disclosure will be described more fullyhereinafter with reference to the accompanying drawings in which likenumerals represent like elements throughout the several figures, and inwhich example embodiments are shown. Embodiments of the claims may,however, be embodied in many different forms and should not be construedas limited to the embodiments set forth herein. The examples set forthherein are non-limiting examples and are merely examples among otherpossible examples.

Referring to FIG. 1, shown in an example of a sewing robot 102. Asillustrated in the example of FIG. 1, the sewing robot 102 can comprisea processor 104, memory 106, a human machine interface or HMI 108,input/output (I/O) devices 110, networking device(s) 112, sewing device114, fabric mover(s) 116, secondary operation device(s) 118, localinterface 120, and sensors 122 (e.g., RGB camera, RGB-D camera, NIRcamera, etc.). The sewing robot 102 can also include a vision basemodule 124, an incoming inspection module 126, inline process module128, and/or quality inspection module 130 which may be executed toimplement various aspects of a vision system.

The vision base module 124 can initiate the incoming inspection module126, inline process module 128, and quality inspection module 130. Theincoming inspection module 126 can capture sensor data prior to anyactions performed by the sewing robot 102 to determine the position of awork piece in a work area of the sewing robot 102 by comparing theresults with sensor profile(s) 132 and robot system profile(s) 134. Theinline process module 128 can capture sensor data while actions arebeing performed by the sewing robot 102 to determine the position of thework piece in the work area by comparing the results with sensorprofile(s) 132 and robot system profile(s) 134. The quality inspectionmodule 130 can capture sensor data after the actions are performed bythe sewing robot 102 to determine the quality of the sewn piece in thework area by comparing the results with sensor profile(s) 132 and robotsystem profile(s) 134.

The processor 104 can be configured to decode and execute anyinstructions received from one or more other electronic devices orservers. The processor 104 can include one or more general-purposeprocessors (e.g., INTEL® or Advanced Micro Devices® (AMD)microprocessors) and/or one or more special purpose processors (e.g.,digital signal processors or Xilinx® System On Chip (SOC) fieldprogrammable gate array (FPGA) processor). The processor 104 may beconfigured to execute one or more computer-readable programinstructions, such as program instructions to carry out any of thefunctions described in this description. Processing circuitry includingthe processor 104 can be configured to execute one or morecomputer-readable program instructions, such as program instructions tocarry out any of the functions described in this description.

The memory 106 can include, but is not limited to, fixed (hard) drives,magnetic tape, floppy diskettes, optical disks, compact disc read-onlymemories (CD-ROMs), and magneto-optical disks, semiconductor memories,such as ROMs, random access memories (RAMs), programmable read-onlymemories (PROMs), erasable PROMs (EPROMs), electrically erasable PROMs(EEPROMs), flash memory, magnetic or optical cards, or other type ofmedia/machine-readable medium suitable for storing electronicinstructions. The memory 106 can comprise modules that can beimplemented as a program executable by processor(s) 104.

The interface(s) or HMI 108 can either accept inputs from users orprovide outputs to the users or may perform both the actions. In onecase, a user can interact with the interfaces using one or moreuser-interactive objects and devices. The user-interactive objects anddevices may comprise user input buttons, switches, knobs, levers, keys,trackballs, touchpads, cameras, microphones, motion sensors, heatsensors, inertial sensors, touch sensors, or a combination of the above.Further, the interfaces can either be implemented as a command lineinterface (CLI), a human machine interface (HMI), a voice interface, ora web-based user-interface, at element 108.

The input/output devices or I/O devices 110 of the sewing robot 102 cancomprise components used to facilitate connections of the processor 104to other devices such as, e.g., a knife device, sewing device 114,fabric mover(s) 116, secondary operation device(s) 118, and/or sensor(s)122 and therefore, for instance, can comprise one or more serial,parallel, small system interface (SCSI), universal serial bus (USB), orIEEE 1394 (i.e. Firewire™) connection elements.

The networking device(s) 112 of the sewing robot 102 can comprisevarious components used to transmit and/or receive data over a network.The networking device(s) 112 can include a device that can communicateboth inputs and outputs, for instance, a modulator/demodulator (i.e.modem), a radio frequency (RF) or infrared (IR) transceiver, atelephonic interface, a bridge, a router, as well as a network card,etc.

The sewing device 114 of the sewing robot 102 can facilitate sewing thework piece material(s) together and can be configured to sew a perimeteror along markings on the work piece material based on tracking agenerated pattern. In additional embodiments, the sewing device 114 caninclude a knife device in order to cut threads, stitches, materials fromthe work piece etc. The fabric mover(s) 116 or material mover(s) canfacilitate moving the product material(s) during the cutting and sewingoperations, at element 116. The secondary operation device(s) 118 caninclude stacking device(s), folding device(s), label manipulationdevice(s), and/or other device(s) that assist with the preparation,making and/or finishing of the sewn product.

The local interface 120 of the sewing robot 102 can be, for example, butnot limited to, one or more buses or other wired or wirelessconnections, as is known in the art. The local interface 120 can haveadditional elements, which are omitted for simplicity, such ascontrollers, buffers (caches), drivers, repeaters, and receivers, toenable communications. Further, the local interface 120 can includeaddress, control, and/or data connections to enable appropriatecommunications among the components, at element 120.

The sensor(s) 122 of the sewing robot 102 can facilitate detecting theposition and movement of the work piece material(s). A system includingone or a plurality of sensors 122 can be used such as, but not limitedto, motion sensors, temperature sensors, humidity sensors, cameras suchas a RGB camera, an RGB-D camera, a stereoscopic camera, a near infrared(NIR) camera, or other image capture devices, microphones,radiofrequency receiver, a thermal imager, a radar device, a lidardevice, an ultrasound device, a speaker, wearable devices etc., atelement 122. The RGB-D camera is a digital camera that can provide color(RGB) and depth information for the pixels in an image. In someembodiments, a single sensor 122 can function to provide a variety ofsensing activities.

As shown in FIG. 1, the sewing robot 102 includes a vision base module124 which can initiate the incoming inspection module 126, inlineprocess module 128, and quality inspection module 130. The incominginspection module 126 can capture sensor data after it enters theworkspace of the sewing robot 102 to determine the position of a workpiece in the work area by comparing the results with sensor profile(s)132 and robot system profile(s) 134. The inline process module 128 cancapture sensor data while actions are being performed by the sewingrobot 102 to determine the position of the work piece in the work areaby comparing the results with sensor profile(s) 132 and robot systemprofile(s) 134. The quality inspection module 130 can capture sensordata after the actions are performed by the sewing robot 102 todetermine the position of the work piece in the work area by comparingthe results with sensor profile(s) 132 and robot system profile(s) 134.The quality inspection module 130 can also evaluate other conditions ofthe work piece.

A sensor profile 132 can be a precomputed sensor model and parameters,such as a model of a detected shape of a sewn product work piece thatcan be used in order to process sensor readings and estimate theposition of a work piece in a work area by comparing the model of thedetected shape to a model of the expected shape. For example, amathematical model of a curve of a work piece where the curve can bedefined as a set of two (or more) points in two (or more) dimensionssuch as a semi-circle curve, right angle curve, etc. The sensor profile132 can include criteria such as, e.g., characteristics, parametersand/or tolerances that can be used for evaluation of a work piece and/orproduct by, e.g., comparison with sensor data of the work piece orproduct. In various embodiments, this can be a precomputed camera modelthat determines, e.g., the size of a work piece, curve of a work piece,irregularities in the work piece, area of the work piece, threadparameters of a work piece, stitch type of a work piece, sewn path of awork piece, and/or size of the finished work piece, to name a few, atelement 132.

A robot system profile 134 can comprise precomputed parameters orlimitations of the robot system 102 such as, e.g., maximum or minimumsize of a work piece, curve sewing capabilities of the robot system,irregularities that can be handled by the robot system, if the workpiece moves outside of the usable work area for the sewing robot 102,thread capabilities of the robot system, stitch capabilities of therobot system, quality checks of the finished work piece such as the sewnpath and size of the work piece, to name a few, at element 134. Therobot system profile 134 can include criteria such as, e.g.,characteristics, parameters, tolerances and/or capabilities that can beused for evaluation of a work piece and/or product by, e.g., comparisonwith sensor data of the work piece or product.

Functioning of the vision base module 124 will now be explained withreference to FIG. 2. One skilled in the art will appreciate that, forthis and other processes and methods disclosed herein, the functionsperformed in the processes and methods may be implemented in differingorder. Furthermore, the outlined steps and operations are only providedas examples, and some of the steps and operations may be optional,combined into fewer steps and operations, or expanded into additionalsteps and operations without detracting from the essence of thedisclosed embodiments.

The flow chart of FIG. 2 shows the architecture, functionality, andoperation of a possible implementation of the vision base module 124.The process begins at 202 with the vision base module 124 detecting anincoming work piece being placed on the work table. The vision basemodule 124 can initiate the incoming inspection module 126 at 204, whichcaptures sensor data, compares the sensor data to a sensor profile 132,which can be precomputed, and a robot system profile 134. If it has beendetermined at 206 that the work piece does not meet specified modelcharacteristics, parameters and/or capabilities of the robot system thenthe process continues to 208 where a corrective action can be taken toaddress the failure to meet the characteristics, parameters orcapabilities, and the process can return to 202 to detect the next workpiece. In some cases, the process may be stopped while the correctiveaction is taken. Corrective actions at 208 can include, but are notlimited to, repositioning the work piece using fabric mover(s) 116,automatically removing the unacceptable work piece from the sewing robot102 work flow, which can be carried out by fabric mover(s) 116 of thesewing robot 102, or stopping the sewing robot 102 until an operatortakes an appropriate action (e.g., manually removing the unacceptablework piece) and restarting the process. The process can be restarted(either automatically or manually) after the corrective action iscomplete at 208.

If the work piece has been deemed adequate or acceptable for furtherprocessing at 206, then the process can continue to 210 where the visionbase module 124 can initiate the inline process module 128. At 210, theinline process module 128 can capture sensor data during processing ofthe work piece, and compare the sensor data to the sensor profile(s) 132(FIG. 1) associated with the sensor data, which can then be compared tothe robot system profile(s) 134 (FIG. 1). The inline process module 128can adjust operation of the sewing robot 102 during processing of thework piece to maintain the processing within acceptable guidelinesassociated with the work piece. For example, sewing along defined seamlines can be continuously or iteratively adjusted during the inlineprocess to maintain the finished product within production tolerances.If the inline process fails to meet specified model characteristics,parameters and/or capabilities, then the work piece can be consideredinadequate or unacceptable and/or the inline process is not complete at212. In that case, corrective action can be taken at 208 as previouslydiscussed, before returning to 202 to detect the next work piece.

If the work piece has been deemed adequate or acceptable, and the inlineprocess is complete at 212, then the vision base model 124 can initiatethe quality inspection module 130 at 214. The quality inspection module130 can capture sensor data, and compare the sensor data to the sensorprofile(s) 132 and the robot system profile(s) 134. If it has beendetermined at 216 that the work piece does not meet specified modelcharacteristics, parameters and/or capabilities then corrective actioncan be taken at 208 as previously discussed, before returning to 202 todetect the next work piece. However, if the work piece has been deemedadequate or acceptable at 216, then the process can return to 202 torepeat the vision base module 124.

It should be noted that these modules are interchangeable and may beused in a different order or different combinations than the oneprovided in this example. For instance the quality inspection module 130and/or inline process module 128 may be initiated by the vision basemodule 124 more than once to ensure the quality of the sewn productbetween various sewing stages during the manufacturing process. Also,various implementations can initiate only one or a reduced combinationof the modules illustrated in FIG. 2. The incoming inspection module 126can be initiated, followed by one or neither of the inline processmodule 128 and/or the quality inspection module being initiated.Similarly, the incoming inspection module 126 can be bypassed afterdetecting an incoming work piece, and one or both of the inline processand/or the quality inspection modules can be initiated. Othercombinations are also possible as can be appreciated.

Functioning of the incoming inspection module 126 will now be explainedwith reference to FIG. 3. One skilled in the art will appreciate that,for this and other processes and methods disclosed herein, the functionsperformed in the processes and methods may be implemented in differingorder. Furthermore, the outlined steps and operations are only providedas examples, and some of the steps and operations may be optional,combined into fewer steps and operations, or expanded into additionalsteps and operations without detracting from the essence of thedisclosed embodiments.

The flow chart of FIG. 3 shows the architecture, functionality, andoperation of a possible implementation of the incoming inspection module126. The process begins at 302 with the incoming inspection module 126being initiated by, e.g., the vision base module 124, at 204 of FIG. 2.The work piece (for example a sewn product such as a piece of fabric ormaterial) can be processed at 304 by the sewing robot 102 (FIG. 1) inwhich the work piece is moved into a work area such as the area of thesewing robot 102 where products are sewn together on a work table. Thework piece can be moved by various fabric mover(s) 116 such as, e.g.,actuators, budgers, end effectors, etc. In various implementations, theprocessing of the work piece can be monitored at 306 by various sensors(1-N) 122, which can collect sensor data on the work piece in order tocomplete the sewing process. For example, the sensors can capture datain which there is a piece of product that needs to be destacked andloaded onto the work table, at 304. The sensors 122 can capture data(e.g., a camera that captures a series of images) of the piece of theproduct on the work area, at 306.

The captured sensor data is then compared to a sensor profile 132, whichcan be precomputed, to determine if incoming inspection criteria aresatisfied at 308. In some embodiments, the series of images capturedfrom a camera can be compared to a size model of the sensor profile inwhich the length and width of the work piece are determined and placedinto a rectangular template (e.g., bounding box). In other embodiments,the series of images captured from a camera can be compared to a curvemodel of the sensor profile in which the shape of the curves of the workpiece is identified. In another embodiment, the series of imagescaptured from a camera can be compared to an irregularities model of thesensor profile in which the irregularities of the work piece areidentified, at 308.

Next at 310, the incoming inspection module 126 can apply the sensorprofile(s) 132 to the system profile(s) 134 to determine if the incominginspection criteria are satisfied. In some embodiments, the rectangularbounding box of the work piece can be compared to maximum and minimumtemplate parameters for the robot system. In other embodiments, thecurves identified on the work piece can be compared to a list ofavailable curves for the robot system 102. In another embodiment, theirregularities identified on the work piece can be compared to a maximumlimit of irregularities for the sewing robot 102, at 310.

It can then be determined at 312 if the process can proceed based uponthe results of the sensor profile(s) 132 in comparison to the robotsystem profile(s) 134. In one implementation, if the rectangulartemplate or bounding box identified in 308 exceeds the maximum templateparameters or characteristics, or does not meet (or satisfy) the minimumtemplate parameters or characteristics, of the robot system profile(s)134, then the work piece is considered unacceptable and returns at 314to the vision base module 124 (FIG. 2) where the result is considered todetermine if the process can proceed at 206. In other implementations,if the identified curves of the work product are not supported by themodel of the robot system profile 134, then the work piece is consideredunacceptable and at 314 returns to the vision base module 124 (FIG. 2)at 206. In another implementation, if the identified irregularities aredetermined to be too great for the model of the robot system profile 134to handle, then the work piece is considered unacceptable at 312 andreturns at 314 to the vision base module 124 (FIG. 2) at 206 where theresult is considered at 206. If it has been determined that the sensorprofile(s) 132, when compared to the robot system profile(s) 134, is notadequate the process ends, at step 312. If the results of the comparisonbetween the sensor profile(s) 132 and robot system profile(s) 134 isdetermined to be acceptable at 312, then the process returns at 314 tothe vision base module 124 at 206.

Functioning of the inline process module 128 will now be explained withreference to FIG. 4. One skilled in the art will appreciate that, forthis and other processes and methods disclosed herein, the functionsperformed in the processes and methods may be implemented in differingorder. Furthermore, the outlined steps and operations are only providedas examples, and some of the steps and operations may be optional,combined into fewer steps and operations, or expanded into additionalsteps and operations without detracting from the essence of thedisclosed embodiments.

The flow chart of FIG. 4 shows the architecture, functionality, andoperation of a possible implementation of the inline process module 128.The process begins at 402 with the inline process module 128 beinginitiated by, e.g., the vision base module 124 at 210 of FIG. 2. Thework piece (for example a sewn product) can be processed at 404 by thesewing robot 102 in which the work piece is moved into the work areasuch as the area of the sewing robot 102 where products are sewntogether on a work table. The work piece can be moved by various fabricmover(s) 116 such as, e.g., actuators, budgers, end effectors, etc. Insome implementations, the processing of the work piece can be monitoredat 406 by various sensors (1-N) 122 that can collect sensor data on thework piece in order to complete the sewing process, at 404. The sensors122 can capture data (e.g., a camera that captures a series of images)of the piece of the product on the work area, at 406.

The captured sensor data can then be compared to a model of the sensorprofile(s) 132 to determine if inline process criteria are satisfied at408. In some embodiments, the series of images captured from a cameracan be compared to an area model of sensor profile(s) 132 in which thearea of the work piece is determined. In other embodiments, the seriesof images captured from the camera can be compared to a thread model ofsensor in which the color of the thread or size of the thread isspecified. In another embodiment, the series of images captured from thecamera can be compared to a stitch type model of sensor profile(s) 132in which the stitch type is specified, at 408.

Next at 410, the inline process module 128 can apply the sensor profileto the robot system profile 134 to determine if the inline processcriteria are satisfied. In some embodiments, the area of the product canbe compared to maximum and minimum area parameters or characteristicsfor the model of the robot system profile 134. In other embodiments, thecolor of the thread or size of the thread can be compared to theavailable thread capabilities of the model of the robot system profile134. In another embodiment, the stitch type can be compared to theavailable stitch capabilities of the model of the robot system profile134, at step 410.

It can then be determined at 412 if the process can proceed based uponthe results of the sensor profile(s) 132 comparison to the robot systemprofile(s) 134. In some implementations, if the shape or area of theproduct, identified in 308 of FIG. 3, do not meet the parameters orcharacteristics of the robot system profile 134 (or the sensor profile132), then the work piece can be considered unacceptable and returns at414 to the vision base module 124 (FIG. 2) where the result isconsidered to determine if the process can proceed at 212. In otherimplementations, if the color or size of the thread of the product arenot supported by the model of the robot system profile 134, then thework piece is unacceptable at 412 and the process returns at 414 to thevision base module 124. In another embodiment, if the stitch type is notsupported by the model of the robot system profile 134, then the workpiece is considered unacceptable at 412 and returns at 414 to the visionbase module 124 (FIG. 2) at 212. If it is determined that the sensorprofile(s) 132 comparison to the robot system profile(s) 134 is notadequate to fulfill the specified model characteristics, parametersand/or capabilities, then the flow returns to the vision base model 124at 414.

If the results of the comparison between the sensor profile(s) 132 androbot system profile(s) 134 is determined to be acceptable at 412, thenit is determined at 416 if the inline process is complete. For example,if the sewn product has completed the necessary sewing operations forthe product fabrication. If the process is not complete at 416, then theprocess returns to 404 to continue processing the work piece. If theresults of the comparison between the sensor profile(s) 132 and robotsystem profile(s) 134 is determined to be acceptable at 412 and theinline process 128 has been completed at 416, then the process returnsto the vision base module 124 at 212.

Functioning of the quality inspection module 130 will now be explainedwith reference to FIG. 5. One skilled in the art will appreciate that,for this and other processes and methods disclosed herein, the functionsperformed in the processes and methods may be implemented in differingorder. Furthermore, the outlined steps and operations are only providedas examples, and some of the steps and operations may be optional,combined into fewer steps and operations, or expanded into additionalsteps and operations without detracting from the essence of thedisclosed embodiments.

The flow chart of FIG. 5 shows the architecture, functionality, andoperation of a possible implementation of the quality inspection module130. The process begins at 502 with the quality inspection module 130being initiated by the vision base module 124, at 214 of FIG. 2. Thework piece (for example a sewn product) can be processed at 504 by thesewing robot 102 (FIG. 1) in which the work piece is moved in the workarea such as the area of a sewing robot 102 where pieces of the productare sewn together on a work table. The work piece can be moved byvarious fabric mover(s) 116 such as, e.g., actuators, budgers, endeffectors, etc. In various embodiments, the processing of the work piececan be monitored at 506 by various sensors (1-N) 122 that collect sensordata on the work piece in order to complete the sewing process. Thesensors capture data (e.g., a camera that captures a series of images)of the piece of the product on the work area, at 506.

The captured sensor data can then be compared to a sensor profile 132 at508 to determine if quality inspection criteria are satisfied. In someembodiments, the series of images captured from a camera can be comparedto a sewn path model of the sensor profile(s) 132 in which the distanceof the sewn path from the edge of the product is determined. In otherembodiments, the series of images captured from a camera can be comparedto a finished size model of the sensor profile(s) 132 in which thedimensions of the completed work piece are specified. In anotherembodiment, images captured from a camera can be used to determine ifother features of the product have been constructed correctly, includingthe placement and attachment of other parts of the product, such assnaps, zippers, flaps, etc. Dimensions of the completed work piece canbe determined from the captured images for comparison, at 508.

Next at 510, the quality inspection module 130 can apply the sensorprofile 132 to the robot system profile 134 at 508 to determine if thequality inspection criteria are satisfied. In some embodiments, thedistance of the sewn path from the edge of the sewn product can becompared to the sewn path parameters or characteristics for the robotsystem 102. In other embodiments, the dimensions of the completed workpiece can be compared to the specified dimensions, parameters orcharacteristics of the finished size model for the robot system 102. Inadditional embodiments, while a sewn product is being processed, thesewing robot 102 can be actively gathering sensor data. This data may beused in a machine learning algorithm. The collected sensor data from thesewn product can be compared to previous sensor data of a high qualitysewn product and of a poor quality sewn product, and the sewn productcan be rated based on the comparison of the machine learning algorithmto determine if the quality of the sewn product meets a predeterminedthreshold, at step 510.

It can then be determined at 512 if the process can proceed based uponthe results of the sensor profile(s) 132 comparison to the robot systemprofile(s) 134. In some implementations, if the distance of the sewnpath from the edge of the product in 308 exceeds or does not meet (orsatisfy) the sewn path parameters or characteristics of the robot systemprofile 134 at 512, then the product is considered unacceptable andreturns at 514 to the vision base module 124 (FIG. 2) where the resultis considered to determine if the process can proceed at 216. In otherimplementations, if the identified curves of the product are notsupported by the model of robot system profile 134, the product can beconsidered unacceptable at 512, and returns at 514 to the vision basemodule 124. In another embodiment, if the dimensions of the completedwork piece do not meet the dimensions, parameters or characteristics fora completed work piece, the product can be unacceptable and returns tothe vision base module 124 at 514. If it is determined at 512 that thesensor profile 132 comparison to the robot system profile 134 is notadequate to fulfill the specified model characteristics, parametersand/or capabilities, then the flow returns to the vision base model 124at 514. If the results of the comparison between the sensor profile 132and robot system profile 134 are determined to be acceptable at 512,then the process returns at 514 to the vision base module 124 at 216 ofFIG. 2, where the flow returns to 202 to detect the next incoming workpiece.

It should be emphasized that the above-described embodiments of thepresent disclosure are merely possible examples of implementations setforth for a clear understanding of the principles of the disclosure.Many variations and modifications may be made to the above-describedembodiment(s) without departing substantially from the spirit andprinciples of the disclosure. All such modifications and variations areintended to be included herein within the scope of this disclosure andprotected by the following claims.

The term “substantially” is meant to permit deviations from thedescriptive term that don't negatively impact the intended purpose.Descriptive terms are implicitly understood to be modified by the wordsubstantially, even if the term is not explicitly modified by the wordsubstantially.

It should be noted that ratios, concentrations, amounts, and othernumerical data may be expressed herein in a range format. It is to beunderstood that such a range format is used for convenience and brevity,and thus, should be interpreted in a flexible manner to include not onlythe numerical values explicitly recited as the limits of the range, butalso to include all the individual numerical values or sub-rangesencompassed within that range as if each numerical value and sub-rangeis explicitly recited. To illustrate, a concentration range of “about0.1% to about 5%” should be interpreted to include not only theexplicitly recited concentration of about 0.1 wt % to about 5 wt %, butalso include individual concentrations (e.g., 1%, 2%, 3%, and 4%) andthe sub-ranges (e.g., 0.5%, 1.1%, 2.2%, 3.3%, and 4.4%) within theindicated range. The term “about” can include traditional roundingaccording to significant figures of numerical values. In addition, thephrase “about ‘x’ to ‘y’” includes “about ‘x’ to about ‘y’”.

Therefore, at least the following is claimed:
 1. A system, comprising: asewing robot comprising a work area; a sensor system configured toobtain images of at least a portion of the work area; a vision basemodule; a robot system profile and a sensor profile corresponding to aproduct capable of being fabricated by the sewing robot; and processingcircuitry comprising a processor, wherein execution of the vision basemodule: detects that a work piece is present in the work area by thesensor system; and applies the sensor profile to the robot systemprofile.
 2. The system of claim 1, wherein execution of the vision basemodule determines a position of the work piece based at least in partupon one or more incoming inspection images obtained of the work pieceby the sensor system.
 3. The system of claim 2, wherein the one or moreincoming inspection images are compared to a size model of the sensorprofile, compared to a curve model of the sensor profile, or compared toan irregularities model of the sensor profile.
 4. The system of claim 3,wherein one or more dimension of the work piece is determined from theone or more incoming inspection images and compared to a boundingtemplate of the size model, and applying the sensor profile to the robotsystem profile comprises comparing the bounding template to maximum andminimum template parameters for the sewing robot.
 5. The system of claim3, wherein at least one curve of the work piece is identified based upona comparison of shapes of the curve model with the work piece in the oneor more incoming inspection images, and applying the sensor profile tothe robot system profile comprises comparing the at least one curveidentified on the work piece to a list of available curves for thesewing robot.
 6. The system of claim 3, wherein at least oneirregularity of the work piece is identified based upon a comparison ofthe irregularities model with the work piece in the one or more incominginspection images, and applying the sensor profile to the robot systemprofile comprises comparing the at least one irregularity identified onthe work piece to a defined irregularity limit for the sewing robot. 7.The system of claim 1, wherein execution of the vision base moduletracks processing of the work piece based at least in part upon one ormore inline process images obtained of the work piece by the sensorsystem.
 8. The system of claim 7, wherein the one or more inline processimages are compared to a curve model of the sensor profile or comparedto a stitch type model of the sensor profile.
 9. The system of claim 8,wherein a shape of the work piece is tracked based upon the one or moreinline process images and the curve model, and applying the sensorprofile to the robot system profile comprises comparing the shape of thework piece to specified parameters for the sewing robot.
 10. The systemof claim 8, wherein stitching of the work piece is tracked based upon acomparison of the stitch type model with the work piece in the one ormore inline process images, and applying the sensor profile to the robotsystem profile comprises comparing the stitching to available stitchcapabilities for the sewing robot.
 11. The system of claim 7, whereinthe one or more inline process images are captured by the sensor systemduring processing of the work piece by the sewing robot.
 12. The systemof claim 7, wherein a corrective action is initiated in response toprocessing of the work piece not being acceptable.
 13. The system ofclaim 12, wherein operation of the sewing robot is adjusted to maintainprocessing of the work piece within production tolerances.
 14. Thesystem of claim 12, wherein the corrective action comprises removing thework piece from the sewing robot.
 15. The system of claim 1, whereinexecution of the vision base module verifies quality of a work productresulting from processing of the work piece, where the quality is basedat least in part upon one or more quality inspection images obtained ofthe work piece by the sensor system.
 16. The system of claim 15, whereinthe one or more quality inspection images are compared to a sewn pathmodel of the sensor profile or compared to a finished size model of thesensor profile.
 17. The system of claim 16, wherein a sewn path of thework product is determined with respect to sewn path parameters from theone or more quality inspection images and the sewn model.
 18. The systemof claim 17, wherein verifying the quality of the work product comprisescomparing a distance of the sewn path to a defined sewn path limit forthe sewing robot.
 19. The system of claim 16, wherein at least onedimension of the work product is identified based upon a comparison ofthe finished size model with the one or more quality inspection images.20. The system of claim 19, wherein verifying the quality of the workproduct comprises comparing the at least one dimension to defineddimension limits for the sewing robot.